---
library_name: transformers
tags:
- text-classification
- spam-detection
- sms
license: apache-2.0
---

# 🛡️ Model Card for `alusci/distilbert-smsafe`

A lightweight DistilBERT model fine-tuned for spam detection in SMS messages. The model classifies input messages as either **spam** or **ham** (not spam), using a custom dataset of real-world OTP (One-Time Password) and spam SMS messages.

---

## Model Details

### Model Description

- **Developed by:** [alusci](https://huggingface.co/alusci)
- **Model type:** Transformer-based binary classifier
- **Language(s):** English
- **License:** Apache 2.0
- **Finetuned from model:** `distilbert-base-uncased`

### Model Sources

- **Repository:** [https://huggingface.co/alusci/distilbert-smsafe](https://huggingface.co/alusci/distilbert-smsafe)

---

## 🛠️ Uses

### Direct Use

- Detect whether an SMS message is spam or ham (OTP or not).
- Useful in prototypes, educational settings, or lightweight filtering applications.

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="alusci/distilbert-smsafe")
result = classifier("Your verification code is 123456. Please do not share it with anyone.")

# Optional: map the label to human-readable terms
label_map = {"LABEL_0": "ham", "LABEL_1": "spam"}
print(f"Label: {label_map[result[0]['label']]} - Score: {result[0]['score']:.2f}")
```

### Out-of-Scope Use

- Not intended for email spam detection or multilingual message filtering.
- Not suitable for production environments without further testing and evaluation.

---

## 🧪 Bias, Risks, and Limitations

- The model may reflect dataset biases (e.g., message structure, language patterns).
- It may misclassify legitimate OTPs or non-standard spam content.
- Risk of false positives in edge cases.

### Recommendations

- Evaluate on your own SMS dataset before deployment.
- Consider combining with rule-based or heuristic systems in production.

---

## 📚 Training Details

### Training Data

- Dataset used: [`alusci/sms-otp-spam-dataset`](https://huggingface.co/datasets/alusci/sms-otp-spam-dataset)
- Binary labels for spam and non-spam OTP messages

### Training Procedure

- **Epochs:** 5
- **Batch Size:** 16 (assumed)
- **Loss Function:** CrossEntropyLoss
- **Optimizer:** AdamW
- **Tokenizer:** `distilbert-base-uncased`

---

## 📈 Evaluation

### Metrics

- Accuracy, Precision, Recall, F1-score on held-out validation set
- Binary classification labels:  
  - `LABEL_0` → ham  
  - `LABEL_1` → spam

### Results

**Evaluation metrics after 5 epochs:**

- **Loss:** 0.2962  
- **Accuracy:** 91.35%  
- **Precision:** 90.26%  
- **Recall:** 100.00%  
- **F1-score:** 94.88%

**Performance:**

- **Evaluation runtime:** 4.37 seconds  
- **Samples/sec:** 457.27  
- **Steps/sec:** 9.15

---

## 🌱 Environmental Impact

- **Hardware Type:** Apple Silicon MPS GPU (Mac)
- **Hours used:** <1 hour (small dataset)
- **Cloud Provider:** None (trained locally)
- **Carbon Emitted:** Minimal due to local and efficient hardware

---

## 🔧 Technical Specifications

### Model Architecture and Objective

- **Base:** DistilBERT
- **Objective:** Binary classification head on pooled output
- **Parameters:** ~66M (same as distilbert)

---

## 📬 Model Card Contact

For questions or feedback, please contact via [Hugging Face profile](https://huggingface.co/alusci).